Staff Machine Learning Engineer

SimpliSafe is hiring a Staff Machine Learning Engineer in Boston, MA (hybrid) to lead the technical direction of its ML data and training platform. In this role, you will shape architecture and multi-quarter plans that help the team scale reliable training workflows for classical machine learning, deep learning, and LLM fine-tuning.

You will own critical platform capabilities end to end, from versioned dataset creation to experimentation, reproducibility, and automation that accelerates model development. The work spans distributed training infrastructure, engineering standards, and operational readiness, with a focus on reliability, reproducibility, and cost efficiency in AWS.

What you’ll do

• Define and own the technical vision, architecture, and multi-quarter roadmap for SimpliSafe’s ML data and training platform.
• Architect and lead delivery of scalable data and feature pipelines that produce reliable, versioned training datasets.
• Build and evolve distributed training infrastructure for classical ML, deep learning, and LLM fine-tuning, including efficient GPU scheduling and utilization.
• Own the experimentation stack, including experiment tracking, model registry, and dataset and model lineage, so models remain reproducible.
• Develop CI/CD and automation for training pipelines to help data scientists move from idea to trained, validated models quickly and safely.
• Partner with data science, data engineering, product, and security leaders to align platform work with business priorities and convert ambiguous needs into executable plans.
• Set org-wide standards and paved paths for data versioning, training reproducibility, and data governance, and drive adoption across teams.
• Lead build-vs-buy evaluations to make durable technology decisions for ML tooling, cloud services, and frameworks.
• Drive reliability and cost efficiency for training and data workloads in AWS, including capacity planning and compute and GPU cost optimization.
• Mentor and grow senior and mid-level engineers through design reviews, code reviews, and technical coaching.
• Participate in the on-call rotation for platform services the team owns and lead postmortems for critical incidents.

What you bring

• 8+ years of experience in software engineering, data engineering, or related fields, including at least 4 years focused on ML infrastructure or MLOps.
• Proven technical leadership delivering ML training platforms, data platforms, or large-scale distributed systems end to end, including cross-team initiative leadership without direct authority.
• Expert-level experience with AWS (or similar public clouds) across compute, storage, networking, and container orchestration.
• Deep experience with CI/CD pipelines, Docker, Kubernetes, and infrastructure-as-code tools such as Terraform and CloudFormation.
• Strong Python proficiency and hands-on experience with ML frameworks including PyTorch and TensorFlow.
• Hands-on experience with distributed training and large-scale data processing such as Ray, Spark, and PyTorch Distributed.
• Deep understanding of experiment tracking, model registries, and data and model versioning using tools like MLflow, Weights & Biases, and DVC.
• Excellent written and verbal communication skills, including writing technical design docs and influencing senior technical and non-technical stakeholders.

Technologies you’ll work with

• AWS, Terraform, CloudFormation
• Python
• PyTorch, TensorFlow
• Ray, Spark
• Docker, Kubernetes, PyTorch Distributed
• MLflow, Weights & Biases, DVC
• CI/CD, LLM fine-tuning, GPU scheduling
• Experiment tracking, model registry, data versioning, model versioning
• Infrastructure-as-code

Benefits

• Mission- and values-driven culture with a safe and inclusive environment.
• Comprehensive total rewards package supporting wellness and providing security for SimpliSafers and their families.
• Free SimpliSafe system and professional monitoring for your home.
• Employee Resource Groups (ERGs) to network, mentor and develop, and advocate for change.

Nice to have

• Experience handling PII data.
• Experience with workflow orchestration tools such as Airflow, Kubeflow, Flyte, or Step Functions.
• Experience with feature stores and data lakehouse technologies such as Feast, SageMaker Feature Store, Delta Lake, or Iceberg.
• Experience building pipelines for large unstructured datasets like video, images, or audio, including labeling and curation workflows.
• Experience with event-driven or streaming architectures such as Kafka or Kinesis.
• Knowledge of cloud security, IAM, data privacy, and compliance best practices for ML data and workloads.

Salary: USD 183,500 - 244,600 per year. Location: Boston, MA (hybrid). Experience: 8+ years.